Journal of Diabetes Science and Technology最新文献

{"title":"Ramadan Fasting, One Less Barrier Raised by Automated Insulin Delivery.","authors":"Cécilia Outenah, Khadijatou Ly Sall, Alfred Penfornis, Coralie Amadou, Dured Dardari","doi":"10.1177/19322968241267227","DOIUrl":"10.1177/19322968241267227","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic Risk Index Profiles and Predictors Among Diverse Adults With Type 1 Diabetes.","authors":"Claire J Hoogendoorn, Raymond Hernandez, Stefan Schneider, Mark Harmel, Loree T Pham, Gladys Crespo-Ramos, Shivani Agarwal, Jill Crandall, Anne L Peters, Donna Spruijt-Metz, Jeffrey S Gonzalez, Elizabeth A Pyatak","doi":"10.1177/19322968231164151","DOIUrl":"10.1177/19322968231164151","url":null,"abstract":"<p><strong>Background: </strong>The Glycemia Risk Index (GRI) was introduced as a single value derived from the ambulatory glucose profile that identifies patients who need attention. This study describes participants in each of the five GRI zones and examines the percentage of variation in GRI scores that is explained by sociodemographic and clinical variables among diverse adults with type 1 diabetes.</p><p><strong>Methods: </strong>A total of 159 participants provided blinded continuous glucose monitoring (CGM) data over 14 days (mean age [SD] = 41.4 [14.5] years; female = 54.1%, Hispanic = 41.5%). Glycemia Risk Index zones were compared on CGM, sociodemographic, and clinical variables. Shapley value analysis examined the percentage of variation in GRI scores explained by different variables. Receiver operating characteristic curves examined GRI cutoffs for those more likely to have experienced ketoacidosis or severe hypoglycemia.</p><p><strong>Results: </strong>Mean glucose and variability, time in range, and percentage of time in high, and very high, glucose ranges differed across the five GRI zones (<i>P</i> values < .001). Multiple sociodemographic indices also differed across zones, including education level, race/ethnicity, age, and insurance status. Sociodemographic and clinical variables collectively explained 62.2% of variance in GRI scores. A GRI score ≥84.5 reflected greater likelihood of ketoacidosis (area under the curve [AUC] = 0.848), and scores ≥58.2 reflected greater likelihood of severe hypoglycemia (AUC = 0.729) over the previous six months.</p><p><strong>Conclusions: </strong>Results support the use of the GRI, with GRI zones identifying those in need of clinical attention. Findings highlight the need to address health inequities. Treatment differences associated with the GRI also suggest behavioral and clinical interventions including starting individuals on CGM or automated insulin delivery systems.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9775380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes.","authors":"Sunghyun Cho, Eleonora M Aiello, Basak Ozaslan, Michael C Riddell, Peter Calhoun, Robin L Gal, Francis J Doyle","doi":"10.1177/19322968231153896","DOIUrl":"10.1177/19322968231153896","url":null,"abstract":"<p><strong>Background: </strong>Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control.</p><p><strong>Methods: </strong>We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records.</p><p><strong>Results: </strong>Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity.</p><p><strong>Conclusions: </strong>The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10795878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interruption of Continuous Glucose Monitoring: Frequency and Adverse Consequences.","authors":"Alexander M Markov, Petra Krutilova, Andrea E Cedeno, Janet B McGill, Alexis M McKee","doi":"10.1177/19322968231156572","DOIUrl":"10.1177/19322968231156572","url":null,"abstract":"<p><strong>Background: </strong>Removal of diabetes devices, including insulin pumps and continuous glucose monitoring (CGM), is a common practice due to hospital policies, interference with imaging studies, medications, and surgical interventions. Furthermore, these devices are inherently prone to malfunction, adhesive failure, and issues with insertion that can lead to a reduction in wear time. Prescription and dispensing practices provide an exact number of sensors per month without redundancy to account for the realities of daily CGM use.</p><p><strong>Methods: </strong>A RedCap survey was completed by adult patients with type 1 or type 2 diabetes (T1D or T2D) who utilize CGM followed in the Diabetes Center at Washington University in St Louis.</p><p><strong>Results: </strong>Of 384 surveys sent, 99 were completed. Participants had a mean age of 54 years, T1D 69%, female 70%, White 96%, non-Hispanic 96%, and a mean duration of diabetes mellitus (DM) 28 years. Of the cohort, 100% used CGM (80.2% Dexcom, 13.5% Freestyle Libre, 6.3% Medtronic), 61% insulin pump, and 41% Hybrid closed-loop (HCL) systems. CGM-related disruption events included device malfunction (in 85.4% of participants), insertion problems (63.5%), and falling off (61.4%). Medical care-related disruption occurred most frequently in the setting of imaging (41.7%), followed by surgery/procedures (11.7%) and hospitalization (4.4%). Adverse glycemic events attributed to CGM disruption, including hyperglycemia and hypoglycemia, occurred ≥4 times in 36.5% and 12.4% of the cohort, respectively.</p><p><strong>Conclusions: </strong>Disruption in CGM use is common. Lack of redundancy of CGM supplies contributes to care disruption and adverse glycemic events.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9383754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AGATA: A Toolbox for Automated Glucose Data Analysis.","authors":"Giacomo Cappon, Giovanni Sparacino, Andrea Facchinetti","doi":"10.1177/19322968221147570","DOIUrl":"10.1177/19322968221147570","url":null,"abstract":"<p><strong>Background: </strong>Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.</p><p><strong>Methods: </strong>Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis.</p><p><strong>Results: </strong>Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.</p><p><strong>Conclusion: </strong>Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418538/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10537674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics of Nocturnal Hypoglycaemic Events and Their Impact on Glycaemia.","authors":"Manuel Eichenlaub, Sükrü Öter, Delia Waldenmaier, Bernd Kulzer, Lutz Heinemann, Ralph Ziegler, Oliver Schnell, Timor Glatzer, Guido Freckmann","doi":"10.1177/19322968241267765","DOIUrl":"10.1177/19322968241267765","url":null,"abstract":"<p><strong>Background: </strong>Nocturnal hypoglycaemia is a burden for people with diabetes, particularly when treated with multiple daily injections (MDI) therapy. However, the characteristics of nocturnal hypoglycaemic events in this patient group are only poorly described in the literature.</p><p><strong>Method: </strong>Continuous glucose monitoring (CGM) data from 185 study participants with type 1 diabetes using MDI therapy were collected under everyday conditions for up to 13 weeks. Hypoglycaemic events were identified as episodes of consecutive CGM readings <70 mg/dl or <54 mg/dl for at least 15 minutes. Subsequently, the time <54 mg/dl (TB54), time below range (TBR), time in range (TIR), time above range (TAR), glucose coefficient of variation (CV), and incidence of hypoglycaemic events were calculated for diurnal and nocturnal periods. Furthermore, the effect of nocturnal hypoglycaemic events on glucose levels the following day was assessed.</p><p><strong>Results: </strong>The incidence of hypoglycaemic events <70 mg/dl was significantly lower during the night compared to the day, with 0.8 and 3.8 events per week, respectively, while the TBR, TB54, and incidence of events with CGM readings <54 mg/dl was not significantly different. Nocturnal hypoglycaemic events <70 mg/dl were significantly longer (60 vs 35 minutes) and enveloped by less rapidly changing glucose levels. On days following nights containing hypoglycaemic events, there was a decrease in TAR, mean CGM glucose level and morning glucose levels and an increase in TB54, TBR, and CV.</p><p><strong>Conclusions: </strong>The results showed that nocturnal hypoglycaemic events are a common occurrence in persons with type 1 diabetes using MDI with significant differences between the characteristics of nocturnal and diurnal events.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fear of Hypoglycemia and Diabetes Distress: Expected Reduction by Glucose Prediction.","authors":"Dominic Ehrmann, Luigi Laviola, Lilli-Sophie Priesterroth, Norbert Hermanns, Nils Babion, Timor Glatzer","doi":"10.1177/19322968241267886","DOIUrl":"10.1177/19322968241267886","url":null,"abstract":"<p><strong>Background: </strong>Extended glucose predictions are novel in diabetes management. Currently, there is no solution widely available. People with diabetes mellitus (DM) are offered features like trend arrows and limited predictions linked to predefined situations. Thus, the impact of extended glucose predictions on the burden of diabetes and person-reported outcomes (PROs) is unclear.</p><p><strong>Methods: </strong>In this online survey, 206 people with type 1 and type 2 diabetes (T1D and T2D), 70.9% and 29.1%, respectively, who participated in the dia·link online panel and were current continuous glucose monitoring (CGM) users, were presented with different scenarios of hypothetical extended glucose predictions. They were asked to imagine how low glucose predictions of 30 minutes and overnight as well as glucose predictions up to 2 hours would influence their diabetes management. Subsequently, they completed the Hypoglycemia Fear Survey II (HFS-II) and the T1 Diabetes Distress Scale (T1-DDS) by rating each item on a 5-point scale (-2: strong deterioration to +2: strong improvement) according to the potential change due to using glucose predictions.</p><p><strong>Results: </strong>For all glucose prediction periods, 30 minutes, up to 2 hours, and at nighttime, the surveyed participants expected moderate improvements in both fear of hypoglycemia (HFS-II: 0.57 ± 0.49) and overall diabetes distress (T1-DDS = 0.44 ± 0.49). The T1-DDS did not differ for type of therapy or diabetes.</p><p><strong>Conclusions: </strong>People with T1D and T2D would see glucose predictions as a potential improvement regarding reduced fear of hypoglycemia and diabetes distress. Therefore, glucose predictions represent a value for them in lowering the burden of diabetes and its management.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycemic Variability in Pregnant Individuals Using Assisted Hybrid Closed-Loop Therapy Versus Sensor-Augmented Pump Therapy.","authors":"Jocelynn King, Elizabeth Buschur, Janet Snell-Bergeon, Laura Pyle, Kathleen Dungan, Rachel Garcetti, Emily Nease, Anna Bartholomew, Carly Johnson, Sarit Polsky","doi":"10.1177/19322968241260050","DOIUrl":"10.1177/19322968241260050","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141419358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ascertaining the Utility of the Glycemia Risk Index for Glucose Outcomes With Hybrid Closed-Loop Therapy in Adolescents and Adults With Type 1 Diabetes.","authors":"Charles Thivolet, Maha Lebbar, Kevin Perge, Marc Nicolino, Sylvie Villar-Fimbel","doi":"10.1177/19322968241258742","DOIUrl":"10.1177/19322968241258742","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11418464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adult's Lived Experience Using the Insulin-Only Bionic Pancreas.","authors":"Kimberly P Garza, Kelsey R Howard, Marissa Feldman, Jill Weissberg-Benchell","doi":"10.1177/19322968241274364","DOIUrl":"https://doi.org/10.1177/19322968241274364","url":null,"abstract":"<p><strong>Background: </strong>The purpose of this study was to assess adults' perspectives after using the insulin-only Bionic Pancreas (BP) during a 13-week pivotal trial. Automated insulin delivery (AID) systems show promise in improving glycemic outcomes and reducing disease burden for those with type 1 diabetes mellitus (T1D). Understanding the lived experience of those using the BP can help to inform education and uptake of AID devices.</p><p><strong>Methods: </strong>Adults ages 19 to 75 (n = 40) participated in age-specific focus groups (19-25, 26-40, 41-64, and 65+) exploring their experiences, thoughts, and feelings about using the BP. Three authors analyzed the focus group data using a hybrid thematic approach.</p><p><strong>Results: </strong>Qualitative analysis of focus groups revealed 14 sub-themes falling into four major themes (diabetes burden, managing glucose levels, daily routine, and user experience). Although participants' overall experience was positive, some reported struggles related to managing out-of-range glucose levels and challenges with the system responding to unique meal schedules and exercise regimens.</p><p><strong>Conclusion: </strong>This study captures patient perspectives regarding their experiences with a new AID system. Patient voice can inform device development and educational approaches for people with T1D. Identifying which patients may benefit the most from wearing this system may facilitate patient/clinician discussions regarding insulin delivery systems that best meet their individualized needs and expectations that may support device uptake and continued use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}